Systems and methods for providing content

ABSTRACT

Systems, methods, and non-transitory computer-readable media can generate a set of candidate content items from a plurality of content items that are available in the social networking system for a first user. A corresponding score for each of the candidate content items can be generated based at least in part on one or more social affinity coefficients corresponding to the first user and a respective second user associated with a candidate content item, wherein a social affinity coefficient provides a quantitative measurement of the strength of a relationship between two users. A first set of content items from the set of candidate content items can be determined based at least in part on the respective scores, wherein content items in the first set are included in a content feed provided to the first user.

FIELD OF THE INVENTION

The present technology relates to the field of content provision. Moreparticularly, the present technology relates to techniques for providingcontent to users.

BACKGROUND

Today, people often utilize computing devices (or systems) for a widevariety of purposes. Users can use their computing devices to, forexample, interact with one another, access content, share content, andcreate content. In some cases, content items can include postings frommembers of a social network. The postings may include text and mediacontent items, such as images, videos, and audio. The postings may bepublished to the social network for consumption by others.

Under conventional approaches, users may post various content items to asocial networking system. In general, content items posted by a firstuser can be included in the respective content feeds of other users ofthe social networking system, for example, that have “followed” thefirst user. By following (or subscribing to) the first user, some or allcontent that is produced, or posted, by the first user may be includedin the respective content feeds of the following users. A user followingthe first user can simply unfollow the first user to prevent new contentthat is produced by the first user from being included in the followinguser's content feed.

SUMMARY

Various embodiments of the present disclosure can include systems,methods, and non-transitory computer readable media configured togenerate a set of candidate content items from a plurality of contentitems that are available in the social networking system for a firstuser. A corresponding score for each of the candidate content items canbe generated based at least in part on one or more social affinitycoefficients corresponding to the first user and a respective seconduser associated with a candidate content item, wherein a social affinitycoefficient provides a quantitative measurement of the strength of arelationship between two users. A first set of content items from theset of candidate content items can be determined based at least in parton the respective scores, wherein content items in the first set areincluded in a content feed provided to the first user.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to provide a set of inputs to a trainedmachine learning model, the set of inputs including at least informationdescribing a content item to be scored and a first social affinitycoefficient corresponding to the first user and an author of the contentitem as measured by the social networking system and obtain a likelihoodof the first user selecting an option to like the content item as outputfrom the trained machine learning model, wherein the score for thecontent item is based at least in part on the likelihood.

In some embodiments, the set of inputs also includes a second socialaffinity coefficient corresponding to the first user and the author ofthe content item as measured by a second social networking system.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to train a machine learning model forpredicting a likelihood of whether the first user will select an optionto like a content item, wherein the score for the content item is basedat least in part on the likelihood.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to generate a set of training examples fortraining the model, wherein at least some of the training examples inthe set include one or more social affinity coefficients that correspondto the first user and another user as features and cause the model to betrained using the set of training examples.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to generate a first training example thatcorresponds to a content item for which the first user selected a likeoption, wherein the first training example includes at least a firstsocial affinity coefficient corresponding to the first user and anauthor of the content item as a feature, the first social affinitycoefficient being measured by the social networking system.

In some embodiments, the first training example also includes a secondsocial affinity coefficient corresponding to the first user and theauthor of the content item as a feature, the second social affinitycoefficient being measured by a second social networking system that isdifferent from the social networking system.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to generate a first training example thatcorresponds to a content item for which a second user being followed bythe first user selected a like option, wherein the first trainingexample includes at least a first social affinity coefficientcorresponding to the first user and the second user as a feature.

In some embodiments, the systems, methods, and non-transitory computerreadable media are configured to generate a first training example thatcorresponds to a content item for which a second user being followed bythe first user selected a like option in both the social networkingsystem and a second social networking system, wherein the first trainingexample includes at least a combined social affinity coefficientcorresponding to the first user and the second user as a feature.

In some embodiments, the combined social affinity coefficient isdetermined based at least in part on i) a first social affinitycoefficient corresponding to the first user and the second user asmeasured by the social networking system and ii) a second socialaffinity coefficient corresponding to the first user and the second useras measured by the second social networking system.

It should be appreciated that many other features, applications,embodiments, and/or variations of the disclosed technology will beapparent from the accompanying drawings and from the following detaileddescription. Additional and/or alternative implementations of thestructures, systems, non-transitory computer readable media, and methodsdescribed herein can be employed without departing from the principlesof the disclosed technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system including an example contentprovider module, according to an embodiment of the present disclosure.

FIG. 2 illustrates an example recommendation module, according to anembodiment of the present disclosure.

FIG. 3 illustrates an example ranking module, according to an embodimentof the present disclosure.

FIG. 4 illustrates an example user data module, according to anembodiment of the present disclosure.

FIG. 5 illustrates an example method for scoring content items,according to an embodiment of the present disclosure.

FIG. 6 illustrates an example method for training a model for scoringcontent items, according to an embodiment of the present disclosure.

FIG. 7 illustrates a network diagram of an example system including anexample social networking system that can be utilized in variousscenarios, according to an embodiment of the present disclosure.

FIG. 8 illustrates an example of a computer system or computing devicethat can be utilized in various scenarios, according to an embodiment ofthe present disclosure.

The figures depict various embodiments of the disclosed technology forpurposes of illustration only, wherein the figures use like referencenumerals to identify like elements. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated in the figures can be employedwithout departing from the principles of the disclosed technologydescribed herein.

DETAILED DESCRIPTION

Approaches for Providing Content

People often utilize computing devices (or systems) for a wide varietyof purposes. Users can use their computing devices to, for example,interact with one another, access content, share content, and createcontent. In some cases, content items can include postings from membersof a social network. The postings may include text and media contentitems, such as images, videos, and audio. The postings may be publishedto the social network for consumption by others.

Under conventional approaches, users may post various content items tothe social networking system. In general, content items posted by afirst user can be included in the respective content feeds of otherusers of the social networking system that have “followed” the firstuser. By following (or subscribing to) the first user, some or allcontent that is produced, or posted, by the first user may be includedin the respective content feeds of the users following the first user. Auser following the first user can prevent new content from the firstuser from being included in the user's content feed by simply“unfollowing” the first user. At any given time, there may be a largenumber of content items that are eligible for presentation to a user.Such content items typically need to be ranked so that content itemsthat are likely to be of interest to the user are prioritized over othercontent items. In some instances, conventional approaches may rankcontent items based on their respective popularity among other users ofthe social networking system. Other approaches may rank content itemsbased on a user's demonstrated interests (e.g., certain types of contentitems in which the user has shown an interest). Such approaches,however, typically do not take into consideration the strength of theuser's relationship with an author of a posted content item to beranked. Accordingly, such conventional approaches may not be effectivein addressing these and other problems arising in computer technology.

An improved approach rooted in computer technology overcomes theforegoing and other disadvantages associated with conventionalapproaches specifically arising in the realm of computer technology. Invarious embodiments, content items that are available for presentationcan be ranked based, in part, on social affinity coefficients thatmeasure strengths of user relationships in one or more social networkingsystems. In one example, a content item that was posted by a second usercan be ranked for a first user based, in part, on a first socialaffinity coefficient that corresponds to a relationship between thefirst user and the second user as measured in a first social networkingsystem. In some embodiments, multiple social affinity coefficients fromdifferent social networking systems can be used to rank content items.For example, a content item that was posted by the second user can beranked for the first user based, in part, on both i) the first socialaffinity coefficient that corresponds to a relationship between thefirst user and the second user as measured in a first social networkingsystem and ii) a second social affinity coefficient that corresponds toa relationship between the first user and the second user as measured ina second social networking system. In various embodiments, a model canbe trained to recommend and/or rank content items for users based onsuch social affinity coefficients. Such coefficients, among otherfeatures, can be aggregated and used as input signals for training themodel. Once trained, the model can provide recommendations and/orrankings of content items for a user based at least in part on thestrengths of the user's relationships across multiple social networkingsystems.

In general, a social affinity coefficient describes a relationshipbetween two users. For example, users that often interact with oneanother in a social networking system will have a higher, or stronger,social affinity coefficient. In contrast, users that rarely interactwith one another in the social networking system will have a lower, orweaker, social affinity coefficient. In some instances, a socialaffinity coefficient describes a bidirectional relationship between twousers. For example, a social affinity coefficient between a first userand a second user will be high, or strong, if the first user often“likes” content items posted by the second user regardless of whetherthe second user ever interacts with the first user. In some instances, asocial affinity coefficient describes a unidirectional relationshipbetween two users. For example, a first user that often “likes” contentitems posted by a second user will have a high, or strong, socialaffinity to the second user. In contrast, if the second user rarely“likes” content items posted by the first user, then the second user'ssocial affinity to the first user will be lower or weaker. Theapproaches described herein contemplate the use of both bidirectionaland unidirectional social affinity coefficients and any combinationthereof.

FIG. 1 illustrates an example system 100 including an example contentprovider module 102, according to an embodiment of the presentdisclosure. As shown in the example of FIG. 1, the content providermodule 102 can include a content module 104, a follow module 106, aninteraction module 108, and a recommendation module 110. In someinstances, the example system 100 can include at least one data store112. The components (e.g., modules, elements, etc.) shown in this figureand all figures herein are exemplary only, and other implementations mayinclude additional, fewer, integrated, or different components. Somecomponents may not be shown so as not to obscure relevant details.

In some embodiments, the content provider module 102 can be implemented,in part or in whole, as software, hardware, or any combination thereof.In general, a module as discussed herein can be associated withsoftware, hardware, or any combination thereof. In some implementations,one or more functions, tasks, and/or operations of modules can becarried out or performed by software routines, software processes,hardware, and/or any combination thereof. In some cases, the contentprovider module 102 can be implemented, in part or in whole, as softwarerunning on one or more computing devices or systems, such as on a useror client computing device. In one example, the content provider module102 or at least a portion thereof can be implemented as or within anapplication (e.g., app), a program, or an applet, etc., running on auser computing device or a client computing system, such as the userdevice 710 of FIG. 7. In another example, the content provider module102 or at least a portion thereof can be implemented using one or morecomputing devices or systems that include one or more servers, such asnetwork servers or cloud servers. In some instances, the contentprovider module 102 can, in part or in whole, be implemented within orconfigured to operate in conjunction with a social networking system (orservice), such as the social networking system 730 of FIG. 7.

The content provider module 102 can be configured to communicate and/oroperate with the at least one data store 112, as shown in the examplesystem 100. The at least one data store 112 can be configured to storeand maintain various types of data. For example, the data store 112 canstore information describing various content that has been posted byusers of a social networking system. In some implementations, the atleast one data store 112 can store information associated with thesocial networking system (e.g., the social networking system 730 of FIG.7). The information associated with the social networking system caninclude data about users, social connections, social interactions,locations, geo-fenced areas, maps, places, events, pages, groups, posts,communications, content, feeds, account settings, privacy settings, asocial graph, and various other types of data. In some implementations,the at least one data store 112 can store information associated withusers, such as user identifiers, user information, profile information,user specified settings, content produced or posted by users, andvarious other types of user data.

The content provider module 102 can be configured to provide users withaccess to content that is posted through a social networking system. Forexample, the content module 104 can provide a first user with access tocontent items through an interface that is provided by a softwareapplication (e.g., a social networking application, browser, etc.)running on a computing device of the first user. The first user can alsouse the interface to post content items to the social networking system.Such content items may include text, images, audio, and videos, forexample. In some embodiments, the software application is configured tosend information describing user actions to the social networkingsystem. Such information can include, for example, which content itemsthe first user has viewed, a respective view duration for each contentitem, and other actions (e.g., like, comment, share, etc.) performed bythe user with respect to a given content item, to name some examples.Such information can be used to generate training examples for trainingmachine learning models as described below.

In various embodiments, other users of the social networking system canaccess content items posted by the first user. In one example, the otherusers can access the content items by searching for the first user, forexample, by user name through an interface provided by a softwareapplication (e.g., a social networking application, browser, etc.)running on their respective computing devices. In some instances, someusers may want to see content items posted by the first user in theirrespective content feed. To cause content items posted by the first userto be included in their respective content feed, a user can select anoption through the interface to subscribe to, or “follow”, the firstuser. The follow module 106 can process the user's request byidentifying the user as a follower of (or “friend” of) the first user inthe social networking system. As a result, some or all content itemsthat are posted by the first user can automatically be included in therespective content feed of the user. If the user decides that they nolonger want to see content from the first user in their respectivecontent feed, the user can select an option through the interface to“unfollow” the first user. As a result, the follow module 106 can removethe association between the user and the first user so that contentitems posted by the first user are no longer included in the contentfeed of the user. In some instances, the user may want to endorse, or“like”, a content item. In such instances, the user can select an optionprovided in the interface to like the desired content item. Theinteraction module 108 can determine when a user likes a given contentitem and can store information describing this relationship. In someinstances, the user may want to post a comment in response to a contentitem. In such instances, the user can select an option provided in theinterface to enter and post the comment for the desired content item.The interaction module 108 can determine when a user posts a comment inresponse to a given content item and can store information describingthis relationship. In some embodiments, such information can be storedin a social graph as described in reference to FIG. 7.

In various embodiments, the recommendation module 110 is configured torank content items for users of the social networking system. Suchcontent items may be available from various sources including, forexample, content items that are posted through the social networkingsystem. More details regarding the recommendation module 110 will beprovided below with reference to FIG. 2.

FIG. 2 illustrates a recommendation module 202, according to anembodiment of the present disclosure. In some embodiments, therecommendation module 110 of FIG. 1 can be implemented with therecommendation module 202. As shown in the example of FIG. 2, therecommendation module 202 can include a candidate generation module 204,a filtering module 206, and a ranking module 208.

In various embodiments, the recommendation module 202 can obtain andrank content items for presentation to users. In some embodiments, theranking of content items is tailored for a particular user. That is, theranked content items will include content items that have beendetermined to be of interest to the user. As mentioned, such contentitems (e.g., animated content, videos, etc.) may have been posted, orotherwise made available, through the social networking system.

In some embodiments, the candidate generation module 204 can determine aset of candidate content items that are eligible for presentation to auser. In some embodiments, the candidate generation module 204identifies as candidates any content items that were posted by usersthat are being followed by the first user. In some embodiments, thecandidate generation module 204 identifies as candidates any contentitems that were liked by users that are being followed by the firstuser. For example, if a second user that is being followed by the firstuser likes a content item, then that content item can be included in theset of candidate content items. In some embodiments, the candidategeneration module 204 identifies as candidates any content items thatwere liked by users whose content items were previously liked by thefirst user. For example, if the first user liked a first content itemthat was posted by a second user and the second user likes a secondcontent item, then the second content item can be included in the set ofcandidate content items. In some embodiments, the candidate generationmodule 204 identifies as candidates any content items that were postedby users that were referenced in one or more search queries that weresubmitted by the first user to the social networking system. Forexample, the first user may have submitted search queries that referencea second user to view the second user's profile and/or content itemsthat have been posted by the second user. In this example, any contentitems posted by the second user can be included in the set of candidatecontent items. In some embodiments, the candidate generation module 204identifies as candidates any content items that were posted by usersthat were referenced in one or more search queries that were submittedby a threshold number of users of the social networking system.

In some embodiments, the candidate generation module 204 identifies ascandidates any content items that were posted by users located in ageographic region (e.g., a point of interest, city, zip code, state,country, continent, geofence, etc.) in which the first user is alsolocated and/or has visited in the past. Such location information may beobtained, for example, from computing devices of users that are used toaccess the social networking system, metadata corresponding to contentitems that were posted by users, and/or information provided by users intheir respective social profiles in the social networking system, toname some examples. In some embodiments, the candidate generation module204 identifies as candidates any content items that were posted by usersthat are located in a geographic region in which users that are followedby the first user (e.g., friends of the first user) are located. Forexample, a content item posted by a third user can be included in theset of candidates if the third user is located in the same geographicregion as a second user that is followed by the first user.

The filtering module 206 can be configured to refine the set ofcandidate content items by removing any content items that satisfycertain filtering criteria. In some embodiments, a machine learningmodel can be trained to predict whether a content item should be removedfrom the set of candidate content items. In some embodiments, the modelcan be trained using training examples such that reference content itemsthat have been hand labeled by quality control personnel as bad contentshould be excluded from the set of candidate content items. Oncetrained, the model can then predict whether a given content item shouldbe excluded from the set of candidate content items, for example, basedon the subject matter captured by the content item. For example, acontent item may be excluded if the content item includes, or has athreshold likelihood of including objectionable content (e.g., nudity,violence, etc.). In another example, a content item may be excluded ifthe content item has a threshold likelihood of being reported as beinginappropriate. After filtering, the remaining content items in the setof candidate content items can be scored and ranked by the rankingmodule 208 for presentation. More details regarding the ranking module208 will be provided below with reference to FIG. 3.

FIG. 3 illustrates an example ranking module 302, according to anembodiment of the present disclosure. In some embodiments, the rankingmodule 208 of FIG. 2 can be implemented with the ranking module 302. Asshown in the example of FIG. 3, the ranking module 302 can include ascoring module 304, a model training module 306, and a feature datamodule 308.

In various embodiments, the scoring module 304 can score each contentitem in a set candidate content items with respect to a user using oneor more trained machine learning models. The model training module 306can train a machine learning model to predict a likelihood of a userperforming some action. For example, a model can be trained to predict alikelihood of a user selecting a “like” option with respect to a contentitem. In another example, a model can be trained to predict a likelihoodof a user posting a comment in response to a content item. Otherexamples include predicting a likelihood that a user will spend athreshold amount of time viewing a content item, a likelihood that auser selects an option to share a content item, and/or a likelihood of auser engaging with the content item in some manner, to name someexamples. When using a model to predict whether a user will perform someaction (e.g., “like”) with respect to a content item, a set of valuesare inputted to the model. These values typically correspond to a set offeatures with which the model was initially trained. One example set ofinputs for ranking a content item for a user can include a user name, acategory describing the content item, a feature vector describing thesubject matter captured by the content item. In this example, the modeloutputs a likelihood of the user performing an action (e.g., “like”)with respect to the content item based at least in part on the inputs tothe model.

In some embodiments, the model training module 306 can train themodel(s) to predict such likelihoods based at least in part on one ormore social affinity coefficients. As mentioned, social affinitycoefficients can measure a relationship between users of various socialnetworking systems. In one example, social affinity coefficients canmeasure a relationship between a user for which the prediction is beingmade and an author (e.g., a different user) of the content item beingscored. In this example, a set of inputs for ranking a content item fora user can include a user name, a category describing the content item,a feature vector describing the subject matter captured by the contentitem, a first social affinity coefficient that measures a relationshipbetween the user and an author of the content item in a first socialnetworking system, and a second social affinity coefficient thatmeasures a relationship between the user and the author in a secondsocial networking system. The model then outputs a likelihood of theuser performing an action (e.g., “like”) with respect to the contentitem based at least in part on the inputs to the model. In suchembodiments, the model training module 306 can train the model(s) usingexamples that each include information corresponding to a set offeatures. One example feature in a first training example can referenceany categories (e.g., games, news, comedy, film, travel, sports, music,etc.), sub-categories, and/or classification (e.g., subject matterclassification) corresponding to a content item that was previouslypresented to the first user. Another example feature in the firsttraining example can indicate whether the first user selected an optionto like the content item. Another feature in the first training examplecan include a first social affinity coefficient that measures thestrength of the relationship between the user and the author of thecontent item in a first social networking system in which the contentitem was posted. Another feature in the first training example caninclude a second social affinity coefficient that measures the strengthof the relationship between the user and the author in a second socialnetworking system. In such embodiments, the model can be trained torelate the first and second social affinity coefficients to the user'saction (e.g., selecting like) or inaction (e.g., not selecting like)when making predictions for other content items.

In some embodiments, the model training module 306 can train themodel(s) based at least in part on one or more social affinitycoefficients between a first user and a second user that is beingfollowed by the first user. For example, a strong social affinitycoefficient between the first user and the second user suggests thatcontent items that are “liked” by the second user are likely to be ofinterest to the first user. In this example, the set of inputs forranking a content item for the first user can include a user name of thefirst user, a category describing the content item, a feature vectordescribing the subject matter captured by the content item, and acombined social affinity coefficient. In some embodiments, the combinedsocial affinity coefficient can be determined based on i) a first socialaffinity coefficient that measures a relationship between the first userand the second user that liked the content item in a first socialnetworking system and ii) a second social affinity coefficient thatmeasures a relationship between the first user and the second user thatliked the same content item in a second social networking system. Thefirst and second social affinity coefficients may be combineddifferently depending on the implementation. For example, the combinedsocial affinity coefficient may be a sum of the first and second socialaffinity coefficients, a difference between the first and second socialaffinity coefficients, an average of the first and second socialaffinity coefficients, or a max of the first and second social affinitycoefficients, to name some examples. The model then outputs a likelihoodof the first user performing an action (e.g., “like”) with respect tothe content item based at least in part on the inputs to the model. Insuch embodiments, the model training module 306 can train the model(s)as described above. However, rather than using social affinitycoefficients describing the relationship between the first user and anauthor of the content item, the training examples can use the combinedsocial affinity coefficient as a feature. Naturally, the model(s)described herein can be trained using information describing additionalsocial affinity coefficients that describe user relationships in othersocial networking systems. Although the examples above describe thetraining and utilization of models for predicting whether a user willselect a “like” option with respect to a content item, such models canbe trained and utilized to predict other types of user engagement. Forexample, the model(s) can be trained to predict whether a user will posta comment in response to a content item, whether the user will view acontent item for a threshold period of time, whether the user will sharea content item with other users of a social networking system, to namesome examples.

In some embodiments, the scoring module 304 can generate a respectivescore for each content item that is eligible to be presented in a user'scontent feed, e.g., each content item in a set of candidate contentitems. A content item's score can be used to determine whether thecontent item is presented in the user's content feed, for example, basedon the content item's score satisfying a threshold score, and also therank in which the content item is presented in the content feed. Thescoring module 304 can score a content item with respect to a user canbased on at least one likelihood that is predicted using a trained modelas described above. For example, the score for a content item may bedetermined based on a likelihood of the user selecting a “like” optionwith respect to the content item. In another example, the score for acontent item may be determined based on a combination of a likelihood ofthe user selecting a “like” option with respect to the content item anda likelihood of the user posting a comment in response to the contentitem. In some embodiments, the respective likelihoods may be weighteddifferently, for example, by assigning respective weights to thelikelihoods.

In some embodiments, each content item is scored with respect to a user.For example, the model training module 306 can train the model(s) tooutput likelihoods that are specific for a given user. That is, a modelcan output a likelihood that was predicted for a given user based onfeedback and/or information corresponding to that user. In someembodiments, users are classified into one or more groups and themodel(s) are trained to output likelihoods that are group-specific. Thatis, a model can output a likelihood that was predicted for a given userbased on feedback and/or information corresponding to a group of usersto which the given user was assigned. In one example, users that haveexhibited similar patterns of interactions (e.g., users that followsimilar users, users that like similar content items, etc.) in a socialnetworking system may be included in the same group. In another example,users may be grouped together based on their age range, gender, lifestage (e.g., user is enrolled in high school, user is enrolled in auniversity, user is at some stage of their career, user is retired,etc.), shared attributes among some proportion of users followed by theuser (e.g., users that tend to follow athletes, etc.), location,language preference, to name some examples.

In various embodiments, the feature data module 308 can be configured toprovide information describing content items, authors of content items,and users for which content items are being ranked. Such information canbe used for both training and utilizing models as described above. Forexample, the feature data module 308 can obtain information such as anycategories, sub-categories, subject matter classifications, and/orfeature vector(s) corresponding to a content item from the first socialnetworking system in which the content item was posted (e.g., the socialnetworking system 730 of FIG. 7). The feature data module 308 can alsoobtain information, e.g., user names, describing the user for which thecontent item is being ranked and the author of the content item from thefirst social networking system. Such information can be used to obtain afirst social affinity coefficient that measures a relationship betweenthe user and the author of the content item in the first socialnetworking system. In some embodiments, the feature data module 308 caninteract with a second social networking system (e.g., the externalsystem 720 of FIG. 7) to obtain a second social affinity coefficientthat measures a relationship between the user and the author of thecontent item in the second social networking system. In suchembodiments, the feature data module 308 may interact with the secondsocial networking system through an interface (e.g., applicationprogramming interface) to provide the second social networking systeminformation describing the author of the content item. The feature datamodule 308 and/or the second social networking system can be configuredto correlate the author's user name in the first social networkingsystem with the author's user name in the second social networkingsystem. Similarly, the user's user name in the first social networkingsystem can be correlated with the user's user name in the second socialnetworking system. Based on such correlations, the second socialnetworking system can provide the second social affinity coefficientthat measures a relationship between the user and the author of thecontent item in the second social networking system.

FIG. 4 illustrates an example user data module 402, according to anembodiment of the present disclosure. As shown in the example of FIG. 4,the user data module 402 can include a user identification module 404and a data access module 406. In some embodiments, the user data module402 is implemented in a second social networking system (e.g., theexternal system 720 of FIG. 7) from which additional social affinitycoefficients are obtained for various user relationships.

In various embodiments, the user data module 402 can be configured toprovide information, e.g., social affinity coefficients, relating tousers of the second social networking system. For example, a firstsocial networking system that is ranking a content item for a user caninteract with the user data module 402 to obtain a social affinitycoefficient between the user and the author of the content item asmeasured in the second social networking system. Such interactions maybe performed using an interface that is provided by the user data module402 such as an application programming interface (API). In this example,the first social networking system may provide user names of the userand the author that are being used in the first social networkingsystem. In some embodiments, the user identification module 404 can beconfigured to correlate the author's user name in the first socialnetworking system with the author's user name in the second socialnetworking system. Similarly, the user identification module 404 cancorrelate the user's user name in the first social networking systemwith the user name being used by the user in the second socialnetworking system. In such embodiments, the data access module 406 canuse the corresponding user names of the user and the author to look uptheir social affinity coefficient as measured by the second socialnetworking system. The data access module 406 can provide the socialaffinity coefficient to the first social networking system, for example,through the API for use in training and/or utilizing the models asdescribed above.

FIG. 5 illustrates an example method 500 for scoring content items,according to an embodiment of the present disclosure. It should beappreciated that there can be additional, fewer, or alternative stepsperformed in similar or alternative orders, or in parallel, within thescope of the various embodiments discussed herein unless otherwisestated.

At block 502, a set of candidate content items can be generated for afirst user from a plurality of content items that are available in thesocial networking system. At block 504, a corresponding score for eachof the candidate content items can be generated based at least in parton one or more social affinity coefficients corresponding to the firstuser and a respective second user associated with a candidate contentitem. A social affinity coefficient can provide a quantitativemeasurement of the strength of a relationship between two users. Atblock 506, a first set of content items from the set of candidatecontent items can be determined based at least in part on the respectivescores, wherein content items in the first set are included in a contentfeed provided to the first user.

FIG. 6 illustrates an example method 600 for training a model forscoring content items, according to an embodiment of the presentdisclosure. It should be appreciated that there can be additional,fewer, or alternative steps performed in similar or alternative orders,or in parallel, within the scope of the various embodiments discussedherein unless otherwise stated.

At block 602, a first social affinity coefficient between a first userand an author of a content item as measured by a first social networkingsystem is determined. At block 604, a second social affinity coefficientbetween the first user and the author of the content item as measured bya second social networking system is determined. At block 606, a set ofinputs are provided to a trained machine learning model. The set ofinputs can include at least information describing the content item tobe scored, the first social affinity coefficient, and the second socialaffinity coefficient. At block 608, a likelihood of the first userselecting an option to like the content item is obtained as output fromthe trained machine learning model. The score for the content item isbased at least in part on the likelihood.

It is contemplated that there can be many other uses, applications,and/or variations associated with the various embodiments of the presentdisclosure. For example, in some cases, user can choose whether or notto opt-in to utilize the disclosed technology. The disclosed technologycan also ensure that various privacy settings and preferences aremaintained and can prevent private information from being divulged. Inanother example, various embodiments of the present disclosure canlearn, improve, and/or be refined over time.

Social Networking System—Example Implementation

FIG. 7 illustrates a network diagram of an example system 700 that canbe utilized in various scenarios, in accordance with an embodiment ofthe present disclosure. The system 700 includes one or more user devices710, one or more external systems 720, a social networking system (orservice) 730, and a network 750. In an embodiment, the social networkingservice, provider, and/or system discussed in connection with theembodiments described above may be implemented as the social networkingsystem 730. For purposes of illustration, the embodiment of the system700, shown by FIG. 7, includes a single external system 720 and a singleuser device 710. However, in other embodiments, the system 700 mayinclude more user devices 710 and/or more external systems 720. Incertain embodiments, the social networking system 730 is operated by asocial network provider, whereas the external systems 720 are separatefrom the social networking system 730 in that they may be operated bydifferent entities. In various embodiments, however, the socialnetworking system 730 and the external systems 720 operate inconjunction to provide social networking services to users (or members)of the social networking system 730. In this sense, the socialnetworking system 730 provides a platform or backbone, which othersystems, such as external systems 720, may use to provide socialnetworking services and functionalities to users across the Internet.

The user device 710 comprises one or more computing devices (or systems)that can receive input from a user and transmit and receive data via thenetwork 750. In one embodiment, the user device 710 is a conventionalcomputer system executing, for example, a Microsoft Windows compatibleoperating system (OS), Apple OS X, and/or a Linux distribution. Inanother embodiment, the user device 710 can be a computing device or adevice having computer functionality, such as a smart-phone, a tablet, apersonal digital assistant (PDA), a mobile telephone, a laptop computer,a wearable device (e.g., a pair of glasses, a watch, a bracelet, etc.),a camera, an appliance, etc. The user device 710 is configured tocommunicate via the network 750. The user device 710 can execute anapplication, for example, a browser application that allows a user ofthe user device 710 to interact with the social networking system 730.In another embodiment, the user device 710 interacts with the socialnetworking system 730 through an application programming interface (API)provided by the native operating system of the user device 710, such asiOS and ANDROID. The user device 710 is configured to communicate withthe external system 720 and the social networking system 730 via thenetwork 750, which may comprise any combination of local area and/orwide area networks, using wired and/or wireless communication systems.

In one embodiment, the network 750 uses standard communicationstechnologies and protocols. Thus, the network 750 can include linksusing technologies such as Ethernet, 802.11, worldwide interoperabilityfor microwave access (WiMAX), 3G, 4G, CDMA, GSM, LTE, digital subscriberline (DSL), etc. Similarly, the networking protocols used on the network750 can include multiprotocol label switching (MPLS), transmissioncontrol protocol/Internet protocol (TCP/IP), User Datagram Protocol(UDP), hypertext transport protocol (HTTP), simple mail transferprotocol (SMTP), file transfer protocol (FTP), and the like. The dataexchanged over the network 750 can be represented using technologiesand/or formats including hypertext markup language (HTML) and extensiblemarkup language (XML). In addition, all or some links can be encryptedusing conventional encryption technologies such as secure sockets layer(SSL), transport layer security (TLS), and Internet Protocol security(IPsec).

In one embodiment, the user device 710 may display content from theexternal system 720 and/or from the social networking system 730 byprocessing a markup language document 714 received from the externalsystem 720 and from the social networking system 730 using a browserapplication 712. The markup language document 714 identifies content andone or more instructions describing formatting or presentation of thecontent. By executing the instructions included in the markup languagedocument 714, the browser application 712 displays the identifiedcontent using the format or presentation described by the markuplanguage document 714. For example, the markup language document 714includes instructions for generating and displaying a web page havingmultiple frames that include text and/or image data retrieved from theexternal system 720 and the social networking system 730. In variousembodiments, the markup language document 714 comprises a data fileincluding extensible markup language (XML) data, extensible hypertextmarkup language (XHTML) data, or other markup language data.Additionally, the markup language document 714 may include JavaScriptObject Notation (JSON) data, JSON with padding (JSONP), and JavaScriptdata to facilitate data-interchange between the external system 720 andthe user device 710. The browser application 712 on the user device 710may use a JavaScript compiler to decode the markup language document714.

The markup language document 714 may also include, or link to,applications or application frameworks such as FLASH™ or Unity™applications, the Silverlight™ application framework, etc.

In one embodiment, the user device 710 also includes one or more cookies716 including data indicating whether a user of the user device 710 islogged into the social networking system 730, which may enablemodification of the data communicated from the social networking system730 to the user device 710.

The external system 720 includes one or more web servers that includeone or more web pages 722 a, 722 b, which are communicated to the userdevice 710 using the network 750. The external system 720 is separatefrom the social networking system 730. For example, the external system720 is associated with a first domain, while the social networkingsystem 730 is associated with a separate social networking domain. Webpages 722 a, 722 b, included in the external system 720, comprise markuplanguage documents 714 identifying content and including instructionsspecifying formatting or presentation of the identified content. Asdiscussed previously, it should be appreciated that there can be manyvariations or other possibilities.

The social networking system 730 includes one or more computing devicesfor a social network, including a plurality of users, and providingusers of the social network with the ability to communicate and interactwith other users of the social network. In some instances, the socialnetwork can be represented by a graph, i.e., a data structure includingedges and nodes. Other data structures can also be used to represent thesocial network, including but not limited to databases, objects,classes, meta elements, files, or any other data structure. The socialnetworking system 730 may be administered, managed, or controlled by anoperator. The operator of the social networking system 730 may be ahuman being, an automated application, or a series of applications formanaging content, regulating policies, and collecting usage metricswithin the social networking system 730. Any type of operator may beused.

Users may join the social networking system 730 and then add connectionsto any number of other users of the social networking system 730 to whomthey desire to be connected. As used herein, the term “friend” refers toany other user of the social networking system 730 to whom a user hasformed a connection, association, or relationship via the socialnetworking system 730. For example, in an embodiment, if users in thesocial networking system 730 are represented as nodes in the socialgraph, the term “friend” can refer to an edge formed between anddirectly connecting two user nodes.

Connections may be added explicitly by a user or may be automaticallycreated by the social networking system 730 based on commoncharacteristics of the users (e.g., users who are alumni of the sameeducational institution). For example, a first user specifically selectsa particular other user to be a friend. Connections in the socialnetworking system 730 are usually in both directions, but need not be,so the terms “user” and “friend” depend on the frame of reference.Connections between users of the social networking system 730 areusually bilateral (“two-way”), or “mutual,” but connections may also beunilateral, or “one-way.” For example, if Bob and Joe are both users ofthe social networking system 730 and connected to each other, Bob andJoe are each other's connections. If, on the other hand, Bob wishes toconnect to Joe to view data communicated to the social networking system730 by Joe, but Joe does not wish to form a mutual connection, aunilateral connection may be established. The connection between usersmay be a direct connection; however, some embodiments of the socialnetworking system 730 allow the connection to be indirect via one ormore levels of connections or degrees of separation.

In addition to establishing and maintaining connections between usersand allowing interactions between users, the social networking system730 provides users with the ability to take actions on various types ofitems supported by the social networking system 730. These items mayinclude groups or networks (i.e., social networks of people, entities,and concepts) to which users of the social networking system 730 maybelong, events or calendar entries in which a user might be interested,computer-based applications that a user may use via the socialnetworking system 730, transactions that allow users to buy or sellitems via services provided by or through the social networking system730, and interactions with advertisements that a user may perform on oroff the social networking system 730. These are just a few examples ofthe items upon which a user may act on the social networking system 730,and many others are possible. A user may interact with anything that iscapable of being represented in the social networking system 730 or inthe external system 720, separate from the social networking system 730,or coupled to the social networking system 730 via the network 750.

The social networking system 730 is also capable of linking a variety ofentities. For example, the social networking system 730 enables users tointeract with each other as well as external systems 720 or otherentities through an API, a web service, or other communication channels.The social networking system 730 generates and maintains the “socialgraph” comprising a plurality of nodes interconnected by a plurality ofedges. Each node in the social graph may represent an entity that canact on another node and/or that can be acted on by another node. Thesocial graph may include various types of nodes. Examples of types ofnodes include users, non-person entities, content items, web pages,groups, activities, messages, concepts, and any other things that can berepresented by an object in the social networking system 730. An edgebetween two nodes in the social graph may represent a particular kind ofconnection, or association, between the two nodes, which may result fromnode relationships or from an action that was performed by one of thenodes on the other node. In some cases, the edges between nodes can beweighted. The weight of an edge can represent an attribute associatedwith the edge, such as a strength of the connection or associationbetween nodes. Different types of edges can be provided with differentweights. For example, an edge created when one user “likes” another usermay be given one weight, while an edge created when a user befriendsanother user may be given a different weight.

As an example, when a first user identifies a second user as a friend,an edge in the social graph is generated connecting a node representingthe first user and a second node representing the second user. Asvarious nodes relate or interact with each other, the social networkingsystem 730 modifies edges connecting the various nodes to reflect therelationships and interactions.

The social networking system 730 also includes user-generated content,which enhances a user's interactions with the social networking system730. User-generated content may include anything a user can add, upload,send, or “post” to the social networking system 730. For example, a usercommunicates posts to the social networking system 730 from a userdevice 710. Posts may include data such as status updates or othertextual data, location information, images such as photos, videos,links, music or other similar data and/or media. Content may also beadded to the social networking system 730 by a third party. Content“items” are represented as objects in the social networking system 730.In this way, users of the social networking system 730 are encouraged tocommunicate with each other by posting text and content items of varioustypes of media through various communication channels. Suchcommunication increases the interaction of users with each other andincreases the frequency with which users interact with the socialnetworking system 730.

The social networking system 730 includes a web server 732, an APIrequest server 734, a user profile store 736, a connection store 738, anaction logger 740, an activity log 742, and an authorization server 744.In an embodiment of the invention, the social networking system 730 mayinclude additional, fewer, or different components for variousapplications. Other components, such as network interfaces, securitymechanisms, load balancers, failover servers, management and networkoperations consoles, and the like are not shown so as to not obscure thedetails of the system.

The user profile store 736 maintains information about user accounts,including biographic, demographic, and other types of descriptiveinformation, such as work experience, educational history, hobbies orpreferences, location, and the like that has been declared by users orinferred by the social networking system 730. This information is storedin the user profile store 736 such that each user is uniquelyidentified. The social networking system 730 also stores data describingone or more connections between different users in the connection store738. The connection information may indicate users who have similar orcommon work experience, group memberships, hobbies, or educationalhistory. Additionally, the social networking system 730 includesuser-defined connections between different users, allowing users tospecify their relationships with other users. For example, user-definedconnections allow users to generate relationships with other users thatparallel the users' real-life relationships, such as friends,co-workers, partners, and so forth. Users may select from predefinedtypes of connections, or define their own connection types as needed.Connections with other nodes in the social networking system 730, suchas non-person entities, buckets, cluster centers, images, interests,pages, external systems, concepts, and the like are also stored in theconnection store 738.

The social networking system 730 maintains data about objects with whicha user may interact. To maintain this data, the user profile store 736and the connection store 738 store instances of the corresponding typeof objects maintained by the social networking system 730. Each objecttype has information fields that are suitable for storing informationappropriate to the type of object. For example, the user profile store736 contains data structures with fields suitable for describing auser's account and information related to a user's account. When a newobject of a particular type is created, the social networking system 730initializes a new data structure of the corresponding type, assigns aunique object identifier to it, and begins to add data to the object asneeded. This might occur, for example, when a user becomes a user of thesocial networking system 730, the social networking system 730 generatesa new instance of a user profile in the user profile store 736, assignsa unique identifier to the user account, and begins to populate thefields of the user account with information provided by the user.

The connection store 738 includes data structures suitable fordescribing a user's connections to other users, connections to externalsystems 720 or connections to other entities. The connection store 738may also associate a connection type with a user's connections, whichmay be used in conjunction with the user's privacy setting to regulateaccess to information about the user. In an embodiment of the invention,the user profile store 736 and the connection store 738 may beimplemented as a federated database.

Data stored in the connection store 738, the user profile store 736, andthe activity log 742 enables the social networking system 730 togenerate the social graph that uses nodes to identify various objectsand edges connecting nodes to identify relationships between differentobjects. For example, if a first user establishes a connection with asecond user in the social networking system 730, user accounts of thefirst user and the second user from the user profile store 736 may actas nodes in the social graph. The connection between the first user andthe second user stored by the connection store 738 is an edge betweenthe nodes associated with the first user and the second user. Continuingthis example, the second user may then send the first user a messagewithin the social networking system 730. The action of sending themessage, which may be stored, is another edge between the two nodes inthe social graph representing the first user and the second user.Additionally, the message itself may be identified and included in thesocial graph as another node connected to the nodes representing thefirst user and the second user.

In another example, a first user may tag a second user in an image thatis maintained by the social networking system 730 (or, alternatively, inan image maintained by another system outside of the social networkingsystem 730). The image may itself be represented as a node in the socialnetworking system 730. This tagging action may create edges between thefirst user and the second user as well as create an edge between each ofthe users and the image, which is also a node in the social graph. Inyet another example, if a user confirms attending an event, the user andthe event are nodes obtained from the user profile store 736, where theattendance of the event is an edge between the nodes that may beretrieved from the activity log 742. By generating and maintaining thesocial graph, the social networking system 730 includes data describingmany different types of objects and the interactions and connectionsamong those objects, providing a rich source of socially relevantinformation.

The web server 732 links the social networking system 730 to one or moreuser devices 710 and/or one or more external systems 720 via the network750. The web server 732 serves web pages, as well as other web-relatedcontent, such as Java, JavaScript, Flash, XML, and so forth. The webserver 732 may include a mail server or other messaging functionalityfor receiving and routing messages between the social networking system730 and one or more user devices 710. The messages can be instantmessages, queued messages (e.g., email), text and SMS messages, or anyother suitable messaging format.

The API request server 734 allows one or more external systems 720 anduser devices 710 to call access information from the social networkingsystem 730 by calling one or more API functions. The API request server734 may also allow external systems 720 to send information to thesocial networking system 730 by calling APIs. The external system 720,in one embodiment, sends an API request to the social networking system730 via the network 750, and the API request server 734 receives the APIrequest. The API request server 734 processes the request by calling anAPI associated with the API request to generate an appropriate response,which the API request server 734 communicates to the external system 720via the network 750. For example, responsive to an API request, the APIrequest server 734 collects data associated with a user, such as theuser's connections that have logged into the external system 720, andcommunicates the collected data to the external system 720. In anotherembodiment, the user device 710 communicates with the social networkingsystem 730 via APIs in the same manner as external systems 720.

The action logger 740 is capable of receiving communications from theweb server 732 about user actions on and/or off the social networkingsystem 730. The action logger 740 populates the activity log 742 withinformation about user actions, enabling the social networking system730 to discover various actions taken by its users within the socialnetworking system 730 and outside of the social networking system 730.Any action that a particular user takes with respect to another node onthe social networking system 730 may be associated with each user'saccount, through information maintained in the activity log 742 or in asimilar database or other data repository. Examples of actions taken bya user within the social networking system 730 that are identified andstored may include, for example, adding a connection to another user,sending a message to another user, reading a message from another user,viewing content associated with another user, attending an event postedby another user, posting an image, attempting to post an image, or otheractions interacting with another user or another object. When a usertakes an action within the social networking system 730, the action isrecorded in the activity log 742. In one embodiment, the socialnetworking system 730 maintains the activity log 742 as a database ofentries. When an action is taken within the social networking system730, an entry for the action is added to the activity log 742. Theactivity log 742 may be referred to as an action log.

Additionally, user actions may be associated with concepts and actionsthat occur within an entity outside of the social networking system 730,such as an external system 720 that is separate from the socialnetworking system 730. For example, the action logger 740 may receivedata describing a user's interaction with an external system 720 fromthe web server 732. In this example, the external system 720 reports auser's interaction according to structured actions and objects in thesocial graph.

Other examples of actions where a user interacts with an external system720 include a user expressing an interest in an external system 720 oranother entity, a user posting a comment to the social networking system730 that discusses an external system 720 or a web page 722 a within theexternal system 720, a user posting to the social networking system 730a Uniform Resource Locator (URL) or other identifier associated with anexternal system 720, a user attending an event associated with anexternal system 720, or any other action by a user that is related to anexternal system 720. Thus, the activity log 742 may include actionsdescribing interactions between a user of the social networking system730 and an external system 720 that is separate from the socialnetworking system 730.

The authorization server 744 enforces one or more privacy settings ofthe users of the social networking system 730. A privacy setting of auser determines how particular information associated with a user can beshared. The privacy setting comprises the specification of particularinformation associated with a user and the specification of the entityor entities with whom the information can be shared. Examples ofentities with which information can be shared may include other users,applications, external systems 720, or any entity that can potentiallyaccess the information. The information that can be shared by a usercomprises user account information, such as profile photos, phonenumbers associated with the user, user's connections, actions taken bythe user such as adding a connection, changing user profile information,and the like.

The privacy setting specification may be provided at different levels ofgranularity. For example, the privacy setting may identify specificinformation to be shared with other users; the privacy settingidentifies a work phone number or a specific set of related information,such as, personal information including profile photo, home phonenumber, and status. Alternatively, the privacy setting may apply to allthe information associated with the user. The specification of the setof entities that can access particular information can also be specifiedat various levels of granularity. Various sets of entities with whichinformation can be shared may include, for example, all friends of theuser, all friends of friends, all applications, or all external systems720. One embodiment allows the specification of the set of entities tocomprise an enumeration of entities. For example, the user may provide alist of external systems 720 that are allowed to access certaininformation. Another embodiment allows the specification to comprise aset of entities along with exceptions that are not allowed to access theinformation. For example, a user may allow all external systems 720 toaccess the user's work information, but specify a list of externalsystems 720 that are not allowed to access the work information. Certainembodiments call the list of exceptions that are not allowed to accesscertain information a “block list”. External systems 720 belonging to ablock list specified by a user are blocked from accessing theinformation specified in the privacy setting. Various combinations ofgranularity of specification of information, and granularity ofspecification of entities, with which information is shared arepossible. For example, all personal information may be shared withfriends whereas all work information may be shared with friends offriends.

The authorization server 744 contains logic to determine if certaininformation associated with a user can be accessed by a user's friends,external systems 720, and/or other applications and entities. Theexternal system 720 may need authorization from the authorization server744 to access the user's more private and sensitive information, such asthe user's work phone number. Based on the user's privacy settings, theauthorization server 744 determines if another user, the external system720, an application, or another entity is allowed to access informationassociated with the user, including information about actions taken bythe user.

In some embodiments, the social networking system 730 can include acontent provider module 746. The content provider module 746 can, forexample, be implemented as the content provider module 102 of FIG. 1. Insome embodiments, the content provider module 746, in whole or in part,may be implemented in a user device 710. In some embodiments, theexternal system 720 can include a user data module 724. The user datamodule 724 can, for example, be implemented as the user data module 402of FIG. 4. As discussed previously, it should be appreciated that therecan be many variations or other possibilities.

Hardware Implementation

The foregoing processes and features can be implemented by a widevariety of machine and computer system architectures and in a widevariety of network and computing environments. FIG. 8 illustrates anexample of a computer system 800 that may be used to implement one ormore of the embodiments described herein in accordance with anembodiment of the invention. The computer system 800 includes sets ofinstructions for causing the computer system 800 to perform theprocesses and features discussed herein. The computer system 800 may beconnected (e.g., networked) to other machines. In a networkeddeployment, the computer system 800 may operate in the capacity of aserver machine or a client machine in a client-server networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. In an embodiment of the invention, the computersystem 800 may be the social networking system 730, the user device 710,and the external system 820, or a component thereof. In an embodiment ofthe invention, the computer system 800 may be one server among many thatconstitutes all or part of the social networking system 730.

The computer system 800 includes a processor 802, a cache 804, and oneor more executable modules and drivers, stored on a computer-readablemedium, directed to the processes and features described herein.Additionally, the computer system 800 includes a high performanceinput/output (I/O) bus 806 and a standard I/O bus 808. A host bridge 810couples processor 802 to high performance I/O bus 806, whereas I/O busbridge 812 couples the two buses 806 and 808 to each other. A systemmemory 814 and one or more network interfaces 816 couple to highperformance I/O bus 806. The computer system 800 may further includevideo memory and a display device coupled to the video memory (notshown). Mass storage 818 and I/O ports 820 couple to the standard I/Obus 808. The computer system 800 may optionally include a keyboard andpointing device, a display device, or other input/output devices (notshown) coupled to the standard I/O bus 808. Collectively, these elementsare intended to represent a broad category of computer hardware systems,including but not limited to computer systems based on thex86-compatible processors manufactured by Intel Corporation of SantaClara, Calif., and the x86-compatible processors manufactured byAdvanced Micro Devices (AMD), Inc., of Sunnyvale, Calif., as well as anyother suitable processor.

An operating system manages and controls the operation of the computersystem 800, including the input and output of data to and from softwareapplications (not shown). The operating system provides an interfacebetween the software applications being executed on the system and thehardware components of the system. Any suitable operating system may beused, such as the LINUX Operating System, the Apple Macintosh OperatingSystem, available from Apple Computer Inc. of Cupertino, Calif., UNIXoperating systems, Microsoft® Windows® operating systems, BSD operatingsystems, and the like. Other implementations are possible.

The elements of the computer system 800 are described in greater detailbelow. In particular, the network interface 816 provides communicationbetween the computer system 800 and any of a wide range of networks,such as an Ethernet (e.g., IEEE 802.3) network, a backplane, etc. Themass storage 818 provides permanent storage for the data and programminginstructions to perform the above-described processes and featuresimplemented by the respective computing systems identified above,whereas the system memory 814 (e.g., DRAM) provides temporary storagefor the data and programming instructions when executed by the processor802. The I/O ports 820 may be one or more serial and/or parallelcommunication ports that provide communication between additionalperipheral devices, which may be coupled to the computer system 800.

The computer system 800 may include a variety of system architectures,and various components of the computer system 800 may be rearranged. Forexample, the cache 804 may be on-chip with processor 802. Alternatively,the cache 804 and the processor 802 may be packed together as a“processor module”, with processor 802 being referred to as the“processor core”. Furthermore, certain embodiments of the invention mayneither require nor include all of the above components. For example,peripheral devices coupled to the standard I/O bus 808 may couple to thehigh performance I/O bus 806. In addition, in some embodiments, only asingle bus may exist, with the components of the computer system 800being coupled to the single bus. Moreover, the computer system 800 mayinclude additional components, such as additional processors, storagedevices, or memories.

In general, the processes and features described herein may beimplemented as part of an operating system or a specific application,component, program, object, module, or series of instructions referredto as “programs”. For example, one or more programs may be used toexecute specific processes described herein. The programs typicallycomprise one or more instructions in various memory and storage devicesin the computer system 800 that, when read and executed by one or moreprocessors, cause the computer system 800 to perform operations toexecute the processes and features described herein. The processes andfeatures described herein may be implemented in software, firmware,hardware (e.g., an application specific integrated circuit), or anycombination thereof.

In one implementation, the processes and features described herein areimplemented as a series of executable modules run by the computer system800, individually or collectively in a distributed computingenvironment. The foregoing modules may be realized by hardware,executable modules stored on a computer-readable medium (ormachine-readable medium), or a combination of both. For example, themodules may comprise a plurality or series of instructions to beexecuted by a processor in a hardware system, such as the processor 802.Initially, the series of instructions may be stored on a storage device,such as the mass storage 818. However, the series of instructions can bestored on any suitable computer readable storage medium. Furthermore,the series of instructions need not be stored locally, and could bereceived from a remote storage device, such as a server on a network,via the network interface 816. The instructions are copied from thestorage device, such as the mass storage 818, into the system memory 814and then accessed and executed by the processor 802. In variousimplementations, a module or modules can be executed by a processor ormultiple processors in one or multiple locations, such as multipleservers in a parallel processing environment.

Examples of computer-readable media include, but are not limited to,recordable type media such as volatile and non-volatile memory devices;solid state memories; floppy and other removable disks; hard diskdrives; magnetic media; optical disks (e.g., Compact Disk Read-OnlyMemory (CD ROMS), Digital Versatile Disks (DVDs)); other similarnon-transitory (or transitory), tangible (or non-tangible) storagemedium; or any type of medium suitable for storing, encoding, orcarrying a series of instructions for execution by the computer system800 to perform any one or more of the processes and features describedherein.

For purposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the description. It will beapparent, however, to one skilled in the art that embodiments of thedisclosure can be practiced without these specific details. In someinstances, modules, structures, processes, features, and devices areshown in block diagram form in order to avoid obscuring the description.In other instances, functional block diagrams and flow diagrams areshown to represent data and logic flows. The components of blockdiagrams and flow diagrams (e.g., modules, blocks, structures, devices,features, etc.) may be variously combined, separated, removed,reordered, and replaced in a manner other than as expressly describedand depicted herein.

Reference in this specification to “one embodiment”, “an embodiment”,“other embodiments”, “one series of embodiments”, “some embodiments”,“various embodiments”, or the like means that a particular feature,design, structure, or characteristic described in connection with theembodiment is included in at least one embodiment of the disclosure. Theappearances of, for example, the phrase “in one embodiment” or “in anembodiment” in various places in the specification are not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, whetheror not there is express reference to an “embodiment” or the like,various features are described, which may be variously combined andincluded in some embodiments, but also variously omitted in otherembodiments. Similarly, various features are described that may bepreferences or requirements for some embodiments, but not otherembodiments.

The language used herein has been principally selected for readabilityand instructional purposes, and it may not have been selected todelineate or circumscribe the inventive subject matter. It is thereforeintended that the scope of the invention be limited not by this detaileddescription, but rather by any claims that issue on an application basedhereon. Accordingly, the disclosure of the embodiments of the inventionis intended to be illustrative, but not limiting, of the scope of theinvention, which is set forth in the following claims.

What is claimed is:
 1. A computer-implemented method comprising:generating, by a social networking system, a set of candidate contentitems from a plurality of content items that are available in the socialnetworking system for a first user; generating, by the social networkingsystem, a set of training examples to train a machine learning model,wherein generating the set of training examples includes: generating, bythe social networking system, a first training example that correspondsto a content item for which the first user selected a like option,wherein the first training example includes at least a first socialaffinity coefficient corresponding to the first user and an author ofthe content item as a feature, the first social affinity coefficientbeing measured by the social networking system, wherein the firsttraining example also includes a second social affinity coefficientcorresponding to the first user and the author of the content item as afeature, the second social affinity coefficient being measured by asecond social networking system that is different from the socialnetworking system; generating, by the social networking system, usingthe trained machine learning model, a corresponding score for each ofthe candidate content items based at least in part on one or more socialaffinity coefficients corresponding to the first user and a respectivesecond user associated with a candidate content item, wherein a socialaffinity coefficient provides a quantitative measurement of the strengthof a relationship between two users; and recommending, by the socialnetworking system, a first set of content items from the set ofcandidate content items based at least in part on the respective scores,wherein content items in the first set are included in a content feedprovided to the first user.
 2. The computer-implemented method of claim1, wherein the generating the corresponding score for each of thecandidate content items further comprises: providing, by the socialnetworking system, a set of inputs to a trained machine learning model,the set of inputs including at least information describing a contentitem to be scored and a first social affinity coefficient correspondingto the first user and an author of the content item as measured by thesocial networking system; and obtaining, by the social networkingsystem, a likelihood of the first user selecting an option to like thecontent item as output from the trained machine learning model, whereinthe score for the content item is based at least in part on thelikelihood.
 3. The computer-implemented method of claim 2, wherein theset of inputs also includes a second social affinity coefficientcorresponding to the first user and the author of the content item asmeasured by a second social networking system.
 4. Thecomputer-implemented method of claim 1, wherein the generating thecorresponding score for each of the candidate content items furthercomprises: training, by the social networking system, a machine learningmodel for predicting a likelihood of whether the first user will selectan option to like a content item, wherein the score for the content itemis based at least in part on the likelihood.
 5. The computer-implementedmethod of claim 4, wherein the training the machine learning modelfurther comprises: generating, by the social networking system, a set oftraining examples for training the model, wherein at least some of thetraining examples in the set include one or more social affinitycoefficients that correspond to the first user and another user asfeatures; and causing, by the social networking system, the model to betrained using the set of training examples.
 6. The computer-implementedmethod of claim 5, wherein the generating the set of training examplesfurther comprises: generating, by the social networking system, a firsttraining example that corresponds to a content item for which a seconduser being followed by the first user selected a like option, whereinthe first training example includes at least a first social affinitycoefficient corresponding to the first user and the second user as afeature.
 7. The computer-implemented method of claim 5, wherein thegenerating the set of training examples further comprises: generating,by the social networking system, a first training example thatcorresponds to a content item for which a second user being followed bythe first user selected a like option in both the social networkingsystem and a second social networking system, wherein the first trainingexample includes at least a combined social affinity coefficientcorresponding to the first user and the second user as a feature.
 8. Thecomputer-implemented method of claim 7, wherein the combined socialaffinity coefficient is determined based at least in part on i) a firstsocial affinity coefficient corresponding to the first user and thesecond user as measured by the social networking system and ii) a secondsocial affinity coefficient corresponding to the first user and thesecond user as measured by the second social networking system.
 9. Asystem comprising: at least one processor; and a memory storinginstructions that, when executed by the at least one processor, causethe system to perform: generating a set of candidate content items froma plurality of content items that are available in the social networkingsystem for a first user; generating a set of training examples to traina machine learning model, wherein generating the set of trainingexamples includes: generating a first training example that correspondsto a content item for which the first user selected a like option,wherein the first training example includes at least a first socialaffinity coefficient corresponding to the first user and an author ofthe content item as a feature, the first social affinity coefficientbeing measured by the social networking system, wherein the firsttraining example also includes a second social affinity coefficientcorresponding to the first user and the author of the content item as afeature, the second social affinity coefficient being measured by asecond social networking system that is different from the socialnetworking system; generating, using the trained machine learning model,a corresponding score for each of the candidate content items based atleast in part on one or more social affinity coefficients correspondingto the first user and a respective second user associated with acandidate content item, wherein a social affinity coefficient provides aquantitative measurement of the strength of a relationship between twousers; and recommending a first set of content items from the set ofcandidate content items based at least in part on the respective scores,wherein content items in the first set are included in a content feedprovided to the first user.
 10. The system of claim 9, wherein thegenerating the corresponding score for each of the candidate contentitems further causes the system to perform: providing a set of inputs toa trained machine learning model, the set of inputs including at leastinformation describing a content item to be scored and a first socialaffinity coefficient corresponding to the first user and an author ofthe content item as measured by the social networking system; andobtaining a likelihood of the first user selecting an option to like thecontent item as output from the trained machine learning model, whereinthe score for the content item is based at least in part on thelikelihood.
 11. The system of claim 10, wherein the set of inputs alsoincludes a second social affinity coefficient corresponding to the firstuser and the author of the content item as measured by a second socialnetworking system.
 12. The system of claim 9, wherein the generating thecorresponding score for each of the candidate content items furthercauses the system to perform: training a machine learning model forpredicting a likelihood of whether the first user will select an optionto like a content item, wherein the score for the content item is basedat least in part on the likelihood.
 13. The system of claim 12, whereinthe training the machine learning model further causes the system toperform: generating a set of training examples for training the model,wherein at least some of the training examples in the set include one ormore social affinity coefficients that correspond to the first user andanother user as features; and causing the model to be trained using theset of training examples.
 14. A non-transitory computer-readable storagemedium including instructions that, when executed by at least oneprocessor of a computing system, cause the computing system to perform amethod comprising: generating a set of candidate content items from aplurality of content items that are available in the social networkingsystem for a first user; generating a set of training examples to traina machine learning model, wherein generating the set of trainingexamples includes: generating a first training example that correspondsto a content item for which the first user selected a like option,wherein the first training example includes at least a first socialaffinity coefficient corresponding to the first user and an author ofthe content item as a feature, the first social affinity coefficientbeing measured by the social networking system, wherein the firsttraining example also includes a second social affinity coefficientcorresponding to the first user and the author of the content item as afeature, the second social affinity coefficient being measured by asecond social networking system that is different from the socialnetworking system; generating, using the trained machine learning model,a corresponding score for each of the candidate content items based atleast in part on one or more social affinity coefficients correspondingto the first user and a respective second user associated with acandidate content item, wherein a social affinity coefficient provides aquantitative measurement of the strength of a relationship between twousers; and recommending a first set of content items from the set ofcandidate content items based at least in part on the respective scores,wherein content items in the first set are included in a content feedprovided to the first user.
 15. The non-transitory computer-readablestorage medium of claim 14, wherein the generating the correspondingscore for each of the candidate content items further causes thecomputing system to perform: providing a set of inputs to a trainedmachine learning model, the set of inputs including at least informationdescribing a content item to be scored and a first social affinitycoefficient corresponding to the first user and an author of the contentitem as measured by the social networking system; and obtaining alikelihood of the first user selecting an option to like the contentitem as output from the trained machine learning model, wherein thescore for the content item is based at least in part on the likelihood.16. The non-transitory computer-readable storage medium of claim 15,wherein the set of inputs also includes a second social affinitycoefficient corresponding to the first user and the author of thecontent item as measured by a second social networking system.
 17. Thenon-transitory computer-readable storage medium of claim 14, wherein thegenerating the corresponding score for each of the candidate contentitems further causes the computing system to perform: training a machinelearning model for predicting a likelihood of whether the first userwill select an option to like a content item, wherein the score for thecontent item is based at least in part on the likelihood.
 18. Thenon-transitory computer-readable storage medium of claim 17, wherein thetraining the machine learning model further causes the computing systemto perform: generating a set of training examples for training themodel, wherein at least some of the training examples in the set includeone or more social affinity coefficients that correspond to the firstuser and another user as features; and causing the model to be trainedusing the set of training examples.